It draws researchers’ attentions how to make use of the log data effectively without paying much for storing them. In this paper, we propose pattern-based deep learning method to extract the features from log datasets and to facilitate its further use at the reasonable expense of the storage performances. By taking the advantages of the neural network and thoughts to combine statistical features with experts’ knowledge, there are satisfactory results in the experiments on some specified datasets and on the routine systems that our group maintains. Processed on testing data sets, the model is 5%, at least, more likely to outperform its competitors in accuracy perspective. More importantly, its schema unveils a new way to mingle experts’ experiences with statistical log parser.

中文翻译：

---

从日志数据集中提取信息的基于模式的深度学习方法

它引起了研究人员的注意，如何有效地利用日志数据而不需要花费太多的存储成本。在本文中，我们提出了基于模式的深度学习方法来从日志数据集中提取特征，并以合理的存储性能为代价促进其进一步使用。通过利用神经网络和思想的优势，将统计特征与专家知识相结合，在一些特定的数据集和我组维护的常规系统上的实验都取得了令人满意的结果。在测试数据集上处理后，该模型至少有 5% 的可能性在准确性方面优于其竞争对手。更重要的是，它的模式揭示了一种将专家经验与统计日志解析器结合起来的新方法。